// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
This is an example illustrating the use of the krls object
from the dlib C++ Library.
The krls object allows you to perform online regression. This
example will train an instance of it on the sinc function.
*/#include<iostream>#include<vector>#include<dlib/svm.h>usingnamespace std;
usingnamespace dlib;
// Here is the sinc function we will be trying to learn with the krls
// object.
doublesinc(double x){if(x ==0)return1;
returnsin(x)/x;
}intmain(){// Here we declare that our samples will be 1 dimensional column vectors. In general,
// you can use N dimensional vectors as inputs to the krls object. But here we only
// have 1 dimension to make the example simple. (Note that if you don't know the
// dimensionality of your vectors at compile time you can change the first number to
// a 0 and then set the size at runtime)
typedef matrix<double,1,1> sample_type;
// Now we are making a typedef for the kind of kernel we want to use. I picked the
// radial basis kernel because it only has one parameter and generally gives good
// results without much fiddling.
typedef radial_basis_kernel<sample_type> kernel_type;
// Here we declare an instance of the krls object. The first argument to the constructor
// is the kernel we wish to use. The second is a parameter that determines the numerical
// accuracy with which the object will perform part of the regression algorithm. Generally
// smaller values give better results but cause the algorithm to run slower. You just have
// to play with it to decide what balance of speed and accuracy is right for your problem.
// Here we have set it to 0.001.
krls<kernel_type>test(kernel_type(0.1),0.001);
// now we train our object on a few samples of the sinc function.
sample_type m;
for(double x =-10; x <=4; x +=1){m(0)= x;
test.train(m, sinc(x));
}// now we output the value of the sinc function for a few test points as well as the
// value predicted by krls object.
m(0)=2.5; cout <<sinc(m(0))<< "" <<test(m)<< endl;
m(0)=0.1; cout <<sinc(m(0))<< "" <<test(m)<< endl;
m(0)=-4; cout <<sinc(m(0))<< "" <<test(m)<< endl;
m(0)=5.0; cout <<sinc(m(0))<< "" <<test(m)<< endl;
// The output is as follows:
// 0.239389 0.239362
// 0.998334 0.998333
// -0.189201 -0.189201
// -0.191785 -0.197267
// The first column is the true value of the sinc function and the second
// column is the output from the krls estimate.
// Another thing that is worth knowing is that just about everything in dlib is serializable.
// So for example, you can save the test object to disk and recall it later like so:
serialize("saved_krls_object.dat")<< test;
// Now let's open that file back up and load the krls object it contains.
deserialize("saved_krls_object.dat")>> test;
// If you don't want to save the whole krls object (it might be a bit large)
// you can save just the decision function it has learned so far. You can get
// the decision function out of it by calling test.get_decision_function() and
// then you can serialize that object instead. E.g.
decision_function<kernel_type> funct = test.get_decision_function();
serialize("saved_krls_function.dat")<< funct;
}